ML-Optimization of Ported Constraint Grammars

Eckhard Bick


Abstract
In this paper, we describe how a Constraint Grammar with linguist-written rules can be optimized and ported to another language using a Machine Learning technique. The effects of rule movements, sorting, grammar-sectioning and systematic rule modifications are discussed and quantitatively evaluated. Statistical information is used to provide a baseline and to enhance the core of manual rules. The best-performing parameter combinations achieved part-of-speech F-scores of over 92 for a grammar ported from English to Danish, a considerable advance over both the statistical baseline (85.7), and the raw ported grammar (86.1). When the same technique was applied to an existing native Danish CG, error reduction was 10% (F=96.94).
Anthology ID:
L14-1228
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
4483–4487
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/24_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Eckhard Bick. 2014. ML-Optimization of Ported Constraint Grammars. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 4483–4487, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
ML-Optimization of Ported Constraint Grammars (Bick, LREC 2014)
Copy Citation:
PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/24_Paper.pdf